NASA/TM—2002-211814
ARL-TR-2744
U.S. ARMY
RESEARCH LABORATORY
Spiral Bevel Gear Damage Detection
Using Decision Fusion Analysis
Pallia J. Dempsey
Glenn Research Center, Cleveland, Ohio
Robert F. Handschnh
U.S. Army Research Laboratory, Glerm Research Center, Cleveland, Ohio
AbdoUahA.Afjeh
University of Toledo, Toledo, Ohio
DISTRIBUTION STATEfVfEMT A
Approved for Public Release
Distribution Unlimited
20030312 103
August 2002
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NASA/TM—2002-211814
U.S. ARMY
RESEARCH LABORATORY
Spiral Bevel Gear Damage Detection
Using Decision Fusion Analysis
Paula J. Dempsey
Glenn Research Center, Cleveland, Ohio
Robert E Handschuh
U.S. Army Research Laboratory, Cleveland, Ohio
AbdollahA.Afjeh
University of Toledo, Toledo, Ohio
Prepared for the
Fifth International Conference on Information Fusion
sponsored by the International Society of Information Fusion
AnnapoUs, Maryland, July 8-11,2002
National Aeronautics and
Space Administration
Glerm Research Center
August 2002
ARL-TR-2744
Available from
NASA Center for Aerospace Infonnation
7121 Standard Drive
Hanover, MD 21076
National Technical Information Service
5285 Port Royal Road
Springfield, VA 22100
Available elecfronically at http: / /gltrs.grcnasa.gov
Spiral Bevel Gear Damage Detection Using
Decision Fusion Analysis
Paula J. Dempsey
National Aeronautics and Space Administration
Glenn Research Center
Cleveland, Ohio 44135
E-mail: paula.j.dempsey@grc.nasa.gov
Robert F. Handschuh
U.S. Army Research Laboratory
Glenn Research Center
Cleveland, Ohio 44135
E-mail: robert.f. handschuh@grc.nasa.gov
Abdollah A. Afjeh
The University of Toledo
Toledo, Ohio 43606
E-mail: AAfleh@UTNet.UToledo.Edu
Abstract - A diagnostic tool for detecting damage to spiral
bevel gears was developed. Two different monitoring
technologies, oil debris analysis and vibration, were
integrated using data fusion into a health monitoring
system for detecting surface fatigue pitting damage on
gears. This integrated system showed improved detection
and decision-making capabilities as compared to using
individual monitoring technologies. This diagnostic tool
was evaluated by collecting vibration and oil debris data
from fatigue tests performed in the NASA Glenn Spiral
Bevel Gear Fatigue Rigs. Data was collected during
experiments performed in this test rig when pitting damage
occurred. Results show that combining the vibration and
oil debris measurement technologies imprbves the
detection ofpitting damage on spiral bevel gears.
1 Introduction
Helicopter transmission integrity is important to helicopter
safety because helicopters depend on the power train for
propulsion, lift, and flight maneuvering. In order to detect
impending transmission failures, the ideal diagnostic tools
used in the health monitoring system would provide realtime health monitoring of the transmission and would
demonstrate a high level of reliable detection to minimize
false alarms. Today's helicopter health monitoring systems
(HUMS) are not yet capable of real-time, on-line, health
monitoring. Current data collected by HUMS is processed
after the flight, and the health status reported is often
plagued with high false alarm rates and undetected faults.
The current fault detection rate of commercially available
HUMS through vibration analysis is 70 percent [1]. False
NASA/TM—2002-211814
warning rates average 1 per hundred flight hours [2]. Often
these systems are complex and require extensive
interpretation by trained diagnosticians [3].
The objective of this research is to integrate oil debris
and vibration based gear damage detection techniques into
a health monitoring system capable of detecting gear
pitting damage with improved detection and decisionmaking capabilities as compared to existing individual
diagnostic tools. This hypothesis will be evaluated
experimentally with vibration and oil debris data collected
fi-om fatigue tests performed in the NASA Glenn Spiral
Bevel Gear Test Facility. Spiral bevel gears are used in
helicopter transmissions to transfer power between
nonparallel intersecting shafts.
Vibration data will be collected from accelerometers
and used to calculate previously validated gear vibration
diagnostic algorithms. Oil debris data will be collected
using a commercially available in-line oil debris sensor.
Oil debris and vibration data will be integrated using fiizzy
logic and decision fusion analysis techniques. The goal of
this research is to provide the end user with clear
information on the health of the gears.
2 Experimental Investigation
Experimental data was recorded from tests performed in
the Spiral Bevel Gear Test facility at NASA Glenn
Research Center. A detailed analysis of this test facility
can be found in the references [4], [5]. The Spiral Bevel
Gear Test Facility is illustrated in Figure 1. The test rig is
used to study the effects of gear material, gear tooth design,
and lubrication on the fatigue strength of gears. The facility
- Accclcromctcr
Figure 1. Spiral Bevel Gear Fatigue Rig.
uses a closed loop torque regenerative system. Two sets of
spiral bevel gears can be tested simultaneously. Fatigue
tests are performed on aerospace quality gears under
controlled operating conditions. The 12 tooth pinion and
36 tooth gear have 5.14 in. (13.06 cm) diametral pitch, 35
degree spiral angle, 1 in. (2.54 cm) face width, 90 degree
shaft angle, and 22.5 degree pressure angle. Tests are
performed for a specified number of hours or until surface
fatigue occurs.
The principal objective of this research is the detection
of pitting damage on spiral bevel gears. Pitting is a fatigue
failure that occurs when small pieces of material break off
from the gear surface, producing pits on the contacting
surfaces [6]. Gears are run until pitting occurs on one or
more teeth. For the purpose of this work two levels of
pitting are defined as initial pitting (pits less than 0.04 cm
diameter and cover less than 25 percent of tooth contact
area) and destructive pitting (pits greater than 0.04 cm
diameter and cover greater than 25 percent of tooth contact
area). If not detected in time, destructive pitting can lead to
a catastrophic transmission failure if the gear teeth crack.
Data was collected once per minute. Shaft speed was
measured by an optical sensor once per each gear shaft
revolution. Torque was measured using a torque meter.
Torque on the gear shaft during testing was on average
75(X) in-lbs. Oil debris data was collected using a
commercially available oil debris sensor that measures the
change in a magnetic field caused by passage of a metal
particle. The amplitude of the sensor output signal is
proportional to the particle mass. The sensor counts the
number of particles, their approximate size based on user
defined particle size ranges, and calculates an accumulated
mass [7]. For these experiments, 14 size ranges from 2251(XX) microns were defined.
Vibration data was measured by two accelerometers
located on the left and right pinion shaft bearing housing.
The location of the right accelerometer is shown in
Figure 1. The location of the left accelerometer is at the
same relative position on the left side of the gearbox. The
accelerometers are lightweight, piezoelectric accelerometers. The left accelerometer measures primarily the
vibration from the left pair of gears and the right
NASA/TM—2002-211814
accelerometer measures primarily the vibration from the
right pair of meshing gears. The test pinion had 12 teeth
with a shaft speed of 10,200 RPM and the gear had 36 teeth
with a shaft speed of 3400 RPM. The meshing frequency
was 2040 cycles/second. The shaft speed was measured on
the test gear shaft. For every revolution of the test gear,
there were three revolutions of the pinion. Vibration data
was sampled at lOOKHz for 2 seconds duration. Time
synchronous averaging was performed from the raw
vibration data for 113 revolutions of the test gear. The
signal time-synchronous average is obtained by taking the
average of the signal in the time domain with each record
starting at the same point in the cycle as determined by the
once per gear revolution tachometer signal [8]. The time
synchronous average data was then used to calculate two
vibration diagnostic parameters FM4 and NA4 Reset.
FM4 was developed to detect changes in the vibration
pattern resulting from fatigue damage on a limited number
of teeth [9]. The theory behind FM4 is that for a gear in
good condition, the difference signal would be
approximately random noise with a Gaussian amplitude
distribution. The standard deviation should be relatively
constant, and normaUzed kurtosis has a value of three.
When a tooth develops a major defect, a peak or series of
peaks appear in the difference signal, causing the kurtosis
value to increase [8]. One problem with the FM4
parameter is that it decreases in sensitivity as the number of
peaks of similar magnitude increase beyond two. For this
reason, NA4 was developed for failures that involve more
than two teeth.
NA4 was developed to detect the onset of fatigue
damage and to continue to react to the damage as it spreads
[10]. However, it does not perform well under flucmating
load conditions. NA4 Reset was developed from NA4 for
applications with load fluctuations [11]. FM4, NA4 and
NA4 Reset are dimensionless parameters with nominal
values of approximately 3. When gear damage occurs, the
values of FM4 and NA4 Reset increase [12].
3 Decision Fusion Analysis
Multisensor data fusion analysis techniques were chosen to
be appUed to gear damage data collected from the two
accelerometers and an oil debris sensor. Multisensor data
fusion is a process similar to methods humans use to
integrate data from multiple sources and senses to make
decisions. In this process, data from multiple sensors are
combined to perform inferences that are not possible from a
single sensor. Commercially available software was used
to perform the analysis [13].
Sensor data can be fused from the raw data level,
feature level, or decision level. Decision level fusion was
chosen to integrate these features because this does not
limit the fusion process to a specific feature. By performing
fijsion at the decision level, new features can be added to
the system or different features can be used without
changing the entire analysis. This allows the most
flexibiUty when applying this process to other condition
based systems since, in most cases, different sensors and
post-processing methods are used.
Features are extracted from the vibration and oil debris
data for input into the data fusion system. Vibration
algorithms FM4 and NA4 Reset are calculated for the two
accelerometers, but only one feature for FM4 and NA4
Reset is input into the model. FM4 and NA4 Reset with the
higher value is input into the model. The larger value of
FM4 and NA4 was chosen to indicate the side with the
most damage and to limit the features to 3. A more
complex model can be developed with additional features
but is outside the scope of this paper. The accumulated
mass measured is used as the feature for the oil debris
sensor [14].
Fuzzy logic membership functions were defined for the
identity declaration step. Fuzzy logic was chosen because it
is tolerant of imprecise data, flexible and can incorporate
the experience of experts. Fuzzy logic starts with a fuzzy
set, extending boolean set theory to a continuous valued
logic via the concept of membership fiinctions valued
between 0 and 1. The truth of any statement becomes a
matter of degree. The fuzzy logic membership functions
identify the damage level on each feature. Levels of
damage are indicated by the oil debris and vibration
features oil debris mass, FM4, and NA4 Reset. Figure 2
shows the data fusion model used for this application.
Decision
Fusion
Identity
Feature
Sensor
t.^«M..uu.l
Riglit
Accel
k
w
Fuzzy
Logic
If (EM4 is DL) and (NA4 is DL) and (debris is DL) then (output is O.K)
If (FM4 is DH) and (NA4 is DH) and (debris is DH) then (output is SHUTDOWN)
If (EM4 is DL) and (NA4 is DL) and (debris is DM) then (output is INSPECT)
If (FM4 is DL) and (NA4 is DH) and (debris is DL) then (output is INSPECT)
If (EM4 is DL) and (NA4 is DL) and (debris is DH) then (output is INSPECT)
If (FM4 is DH) and (NA4 is DL) and (debris is DL) then (output is O.K)
If (FM4 is DH) and (NA4 is DL) and (debris is DM) then (output is INSPECT)
If (FM4 is DH) and (NA4 is DH) and (debris is DL) then (output is INSPECT)
If (FM4 is DH) and (NA4 is DL) and (debris is DH) then (output is SHUTDOWN)
If (FM4 is DH) and (NA4 is DH) and (debris is DM) then (output is INSPECT)
If (EM4 is DL) and CNA4 is DH) and (debris is DH) then (output is SHUTDOWN)
If (FM4 is DL) and (NA4 is DH) and (debris is DM) then (output is INSPECT)
-
i
Left
Accel
Right
Accel
Decision
Level
Fusion
t
NA4
Max
"fe
w
Fuzzy
LoRic
>
Gear
State
-W
i
Left
Accel
Oil
Debris
Sensor
Table 1. Rules for Bevel Rig Data Fusion Model
t
FM4
Max
-
Mass
(mg)
^
-^
Fuzzy
Losic
->
J
Figure 2. Decision Level Fusion Model.
NASA/TM—2002-211814
Fuzzy logic is used to identify the damage level on
each feature and to perform the decision level fusion
process on the features. Although other data fusion
techniques are available, fuzzy logic was chosen based on
the results of several studies to compare production rules,
fuzzy logic and neural nets. One study found fuzzy logic
the most robust when monitoring transitional failure data
on a gearbox [15]. Another study comparing automated
reasoning techniques for condition-based maintenance
found fuzzy logic more flexible than standard logic by
making allowances for unanticipated behavior [16]. The
fuzzy logic process consists of converting each piece of
input data to degrees of membership, aggregating all
outputs into a single fiizzy set, then converting fuzzy
membership information into a single output.
Membership functions for the features are defined as
levels of damage. Levels of damage are damage low (DL),
damage medium (DM), and damage high (DH). The levels
of damage for each feature are as follows: oil debris mass
(DL, DM, DH), NA4 Reset (DL, DH), and FM4 (DL, DH).
Membership functions and rules for the membership
functions were developed based on analysis of this
experimental data and the experience of the diagnostician.
A detailed description of the process used to define the
membership functions is referenced [17]. The membership
functions and rules used for this analysis are shovra in
Table 1 and Figure 3. Triangular membership functions
were used because of the ease of modifying the
membership functions for other geared applications. The
only parameters that need to be changed are the
input/output scaling factors on the membership functions.
Decision level fusion is then performed integrating
membership functions with fuzzy logic rules. The output is
the state of the gear. The output of the system is defined as
the state of the gear and possible action by the end user.
The three states of the gear are O.K. (no gear damage),
inspect (initial pitting), and shutdovm due to damage
(severe destructive pitting).
1.00c
1 0.75 -
DL
DM
/
S
1 0.50-J
DH
\
V(
/V
/ \
L_/
\
0.25
0.(K) -
80
120
160
Oil debris mass (nic)
DL
240
DH
1.0 1.S- 0.8 1
f
i 0.6 1
S 0.4 4
The right side vibration features and oil debris
features for experiment 3 are plotted on Figure 4. Although
vibration data was collected on both sides of the gearbox,
the vibration feature on the right side began to substantially
increase during the test, indicating damage was beginning
to occur on the right side of the test rig. The gears were
inspected at test completion and damage to two teeth on the
right side pinion is shown in Figure 4. Figure 4 also shows
the output of the decision fusion model for this experiment.
The output of the model indicates three states of the gear
based on 3 shades of gray: O.K; inspect; and shutdown.
The X-axis, labeled reading number, is the same on both the
feature plot and the output plot. The reading number is
also the run time in minutes since data was collected once
per minute. If the model was available on-line during
testing, the gears would have been shutdown for inspection
at approximately reading 1500, since the output indicates to
inspect the gears due to damage. If initial pitting was of
interest to the end-user of the diagnostic tool, the oil debris
membership function or rules could be adjusted to be more
sensitive to this type of failure.
20
50
0.2 J
0.00
5
10
15
20
25 30 35
N.M Reset
40
43
50
FM4
NA4 Reset
Oil debris
55
Figure 3. Feature Membership Functions.
4 Discussion of Results
The results discussed in this section are based on data
collected during 6 experiments. During experiments 1 and
2, only oil debris data was collected. And, during
experiment 2, only the total debris at test completion was
measured. At test completion of experiment 1, initial
pitting was observed on one pinion tooth.
At test
completion of experiment 2, destructive pitting occurred on
2 teeth on the right pinion and initial pitting occurred on
one tooth on the left pinion. This data was used to define
the oil debris feature membership functions. Oil debris and
vibration data was collected for experiments 3 through 6
and is discussed in the following paragraphs. The amount
of debris measured at test completion for all 6 experiments
is shown in Table 2.
Table 2. Debris at test completion.
Experiment Readings
Mass
1
29496
42
2
NA
104.3
3
1835
42.4
4
6073
26.5
5
10569
41.2
6
4840
137.7
NASAyTN^-2002-211814
0.9
',
0.8
-0.7
f 0.6
Shttidowiv
2 0.5
1 0.4
Inspect
^ 0.3
0.2
0.1
0.0
O.K.
JL
0
500
1000
Reading number
1500
2000
Figure 4. Vibration oil debris features and damage
to right pinion teeth for bevel test 3.
30
200160
— FM4
— N.M rese
-— Oil debris
'
/
24
I
a:
S 120
CO
18 ^,
Eg
cc
z
i
12 -S
80
40-
f
,
N--.
0
800
1600
„
5K
1
<t
K
b.
,y
2400 3200 4000
Reading number
4800
5600
100
6000
10000
60C
12000
Reading numlier
6400
-ri*%-i*iii
S'
1
0.9
0.9 -fr:"
0.8
S^Bfii^^fi
0.8- hWm
WM^tff
„ 0.73
t 0.6
& 0.6-
in>!pect
2 0.5
Bi
IT-"-
2 0.5-
§ 0.4
§ 0,4-
s
^ 0.3
0.2
O.K.
0.30.2-
0.1
0^
0.1 -
1
1
0
800
1600
2400
3200
4000
4800
5600
6400
Reading Number
X
02000
4000
6000
8000
10000
12000
Reading number
Figure 5. Vibration oil debris features and damage to
left pinion teeth for bevel test 4.
Figure 6. Vibration oil debris features and damage
to left pinion teeth for bevel test 5.
The left side vibration features and oil debris features
for experiment 4 are plotted on Figure 5. For this
experiment, the vibration feature calculated from the left
accelerometer began to substantially increase indicating
damage was beginning to occur to the gears on the left side
of the test rig. The gears were inspected at reading 5742
and pitting began on one tooth on the left pinion. The gears
were inspected at test completion and the damage to the
one tooth on the left side pinion is shown in Figure 5.
Figure 5 also shows the output of the decision fusion model
for this experiment, indicating to inspect the rig due to
damage.
The left side vibration features and oil debris features
for experiment 5 are plotted on Figure 6. For this
experiment, the vibration feature calculated from the left
accelerometer began to substantially increase indicating
damage was begiiming to occur to the gears on the left side
of the test rig. The gears were inspected at test completion
and damage to one tooth on the left side pinion is shown in
Figxire 6. Figure 6 also shows the output of the decision
fiision model for this experiment. The output indicates to
inspect the gears due to damage at approximately reading
9200.
The right side vibration features and oil debris features
for experiment 6 are plotted on Figure 7. Although
vibration data was collected on both sides of the gearbox,
the vibration feature on the right side began to substantially
increase early on in the test. The gears were inspected at
reading 99 and initial pitting was beginning to occur on the
right side of the test rig. Initial pitting was triggered by
vibration algorithm NA4, since debris membership
functions were not as sensitive to initial pitting. The gears
were inspected at test completion and destructive pitting to
one tooth on the right side pinion is shown in Figure 7.
Figure 7 also shows the output of the decision fusion model
for this experiment. Inspect was indicated early on in the
NASA/TM—2002-211814
100
00
80
70 .
eo
— FM4
NA4
— Oil debris
50
10
30
20
10
0
Figure 8. Damage to right pinion bearing for bevel test 6.
Rctding Humlier
failures, the model can be modified to recognize
unanticipated failures. The bearing failure also reinforces
the importance of fusing different measurement
technologies for damage detection since each have
strengths and weaknesses for different applications.
5 Conclusions
1000
2000
3000
4000
5000
Reading number
Figure 7. Vibration oil debris features and damage to right
pinion teeth for bevel test 6.
test and shutdown was indicated at approximately reading
2600. At test completion, it was found that damage also
occurred on the pinion bearing. The damage is shown in
Figure 8. Although the sensitivity of the parameter can be
adjusted to be more sensitive to different levels of damage,
the parameter must be defined on the type of failure die end
user is trying to detect. The vibration features are specific
to gear damage detection. Additional features and logic
must be programmed into the model to differentiate
between gear and bearing failures.
Additional vibration diagnostics for bearing damage
detection can be added to the model to predict future
failures. As more data is acquired on different types of
NASA/TM—2002-211814
After review of the data from these experiments, the
advantage of fusing the features of different measurement
technologies is evident. The output gives clear information
to the end user when making a decision based on the data.
As a diagnostician, it is important to identify the end user
of the diagnostic tool early on in the process. The end user
of most hehcopter transmission diagnostic tools is the
technician that determines if maintenance is required on the
transmission. If the tool being developed requires hours of
analysis and large amounts of stored data to determine the
health of the system, it is probably not feasible for this
application. It is thus important to keep the man-machine
interface in mind when designing diagnostic tools.
The model developed can incorporate the knowledge
of the diagnostician into a system that can be used to make
clear decisions on the status of the geared system relieving
the end user of interpreting large amounts of sensor data.
This research demonstrated integration of two
measurement technologies, oil debris analysis and
vibration, results in a system with improved damage
detection and decision-making capabiUties. Understanding
the strengths, weaknesses and constraints of each
measurement technology, then capitaUzing on these
strengths via data fusion, is key to the development of
future health monitoring systems.
6 References
[1] Larder, B.D. (1999): Helicopter HUM/FDR: Benefits
and Developments. Proceedings of the 55th AHS
International Annual Forum, American Helicopter
Society, Alexandria, VA.
[2] Stewart, Ronald M.; and Ephraim, P. (1997):
Advanced HUMS and Vehicle Management Systems
Implemented Through an IMA Architecture. Annual
Forum Proceedings of the American Helicopter
Society, vol. 52IV2, pp. 1257-1266.
[3] Pouradier, Jean-Marc; and Trouve, Michel (2001): An
Assessment of Eurocopter Experience in HUMS
Development and Support. Proceedings of the 57th
AHS International Annual Forum, American
Helicopter Society, Alexandria, VA.
[4] Handschuh, Robert F. (1995): Thermal Behavior of
Spiral Bevel Gears. NASA TlVl-106518.
[5] Handschuh, Robert F. (2001): Testing of Face-Milled
Spiral Bevel Gears at High-Speed and Load.
NASA/TM—2001-210743.
[6] Townsend, Dennis P. (1991): Dudley's Gear
Handbook. McGraw-Hill, New York, NY.
[7] Howe, B.; and Muir, D. (1998): In-Line Oil Debris
Monitor (ODM) for Helicopter Gearbox Condition
Assessment. AD-a347 503.
Defense Technical
Information Center, Ft. Belvoir, VA.
[8] Zakrajsek, James J. (1989): An Investigation of Gear
Mesh Failure Prediction Techniques. NASA
TM-102340.
[9] Stewart, R.M. (1977): Some Useful Data Analysis
Techniques for Gearbox Diagnostics. Machine Health
Monitoring Group Report MHM/R/10/77, University
of Southampton.
[10] Zakrajsek, J.J.; Townsend, D.P.; and Decker, H.J.
(1993): An Analysis of Gear Fauh Detection Methods
as Applied to Pitting Fatigue Failure Data. NASA
TM-105950.
NASA/TM—2002-211814
[11] Dempsey, Paula J.; and Zakrajsek, James J. (2001):
Minimizing Load Effects on NA4 Gear Vibration
Diagnostic Parameter. NASA/TM—2001-210671.
[12] Zakrajsek, James J.; Handschuh, R.F.; and
Decker,H.J. (1994): Application of Fault Detection
Techniques to Spiral Bevel Gear Fatigue Data. NASA
TM-106467.
[13] Fuzzy Logic Toolbox for use with MATLAB®
(1998): Mathworks, Inc., Natick, MA.
[14] Dempsey, Paula J. (2001): Gear Damage Detection
Using Oil Debris Analysis. NASA/TM—2001210936.
[15] Hall, David L.; Garga, A.K.; and Stover, J. (1999):
Machinery Fault Classification: The Case for a
Hybrid Fuzzy Logic Approach. Society for
Machinery Failure Prevention Technology, H.C.
Pusey, S.C. Pusey, and W.R. Hobbs, eds., Haymarket,
VA, pp. 241-252.
[16] McGonigal, Daniel Lewis (1997): A Comparison of
Automated Reasoning Techniques for ConditionBased Maintenance. M.A. Thesis, Pennsylvania State
Univ.
[17] Dempsey, Paula J. (2002): Integrating Oil Debris and
Vibration Measurements for Intelligent Machine
Health Monitoring. PhD Thesis, The University of
Toledo.
Form Approved
REPORT DOCUMENTATION PAGE
OMB No. 0704-0188
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1. AGENCY USE ONLY (Leave Wan/t)
2. REPORT DATE
3. REPORT TYPE AND DATES COVERED
August 2002
Technical Memorandum
4. TITLE AND SUBTITLE
S. FUNDING NUMBERS
Spiral Bevel Gear Damage Detection Using Decision Fusion Analysis
WU-728-30-10-00
1L162211A47A
6. AUTHOR(S)
Paula J. Dempsey, Robert F. Handschuh, and Abdollah A. AQeh
7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)
8. PERFORMING ORGANIZATION
REPORT NUMBER
National Aeronautics and Space Administration
John H. Glenn Research Center at Lewis Field
Cleveland, Ohio 44135-3191
E-13356
9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES)
10. SPONSORING/MONITORING
AGENCY REPORT NUMBER
National Aeronautics and Space Administration
Washington, DC 20546-0001
and
U.S. Army Research Laboratory
Adelphi. Maryland 20783-1145
NASA TM—2002-211814
ARL-TR-2744
11. SUPPLEMENTARY NOTES
Prepared for the Fifth International Conference on Information Fusion sponsored by the International Society of Information Fusion, Annapolis, Maryland, July 8-11, 2002. Paula J. Dempsey, NASA Glenn Research Center; Robert F.
Handschuh, U.S. Army Research Laboratory, NASA Glenn Research Center; Abdollah A. Afjeh, University of Toledo,
Toledo, Ohio. Responsible person, Paula J. Dempsey, organization code 5950, 216-433-3398.
12a. DISTRIBUTION/AVAILABILITY STATEMENT
Unclassified - Unlimited
Subject Category: 01
12b. DISTRIBUTION CODE
Distribution: Nonstandard
Available electronically at http://gltrs. grcnasa. gov
This publication is available from the NASA Center for AeroSpace Information. 301-621-0390.
13. ABSTBACT (Maximum 200 words)
A diagnostic tool for detecting damage to spiral bevel gears was developed. Two different monitoring technologies, oil
debris analysis and vibration, were integrated using data fusion into a health monitoring system for detecting surface
fatigue pitting damage on gears. This integrated system showed improved detection and decision-making capabiUties as
compared to using individual monitoring technologies. This diagnostic tool was evaluated by collecting vibration and oil
debris data from fatigue tests performed in the NASA Glenn Spiral Bevel Gear Fatigue Rigs. Data was collected during
experiments performed in this test rig when pitting damage occurred. Results show that combining the vibration and oil
debris measurement technologies improves the detection of pitting damage on spiral bevel gears.
14. SUBJECT TERMS
15. NUMBER OF PAGES
Gears; Damage detection; Vibration; Wear debris; Data fusion; Fuzzy logic
17. SECURPTY CLASSIFICATION
OF REPORT
Unclassified
NSN 7540-01-280-5500
18. SECURITY CLASSIFICATION
OF THIS PAGE
Unclassified
19. SECURITY CLASSIFICATION
OF ABSTRACT
13
16. PRICE CODE
20. LIMITATION OF ABSTRACT
Unclassified
Standard Form 298 (Rev. 2-89)
Prescribed by ANSI Std. Z39-18
298-102